Commercial Broiler Feed Additive Decision Support

Introduction

The model deployed on this page is an example illustrating the use of
(limited-memory) influence diagrams to support decision making on the
use of a feed additive in poultry production (commercial broiler).
The decision considered by the farmer is whether or not to use a feed
additive in commercial broiler production. This decision is assumed to
be made after the house has been emptied and cleaned before filling
the house with a new set of birds.

We assume the feed additive is available in crude and purified
form. Furthermore, we assume the farmer can decide between providing
the feed additive in different time frames, e.g., for the first two
weeks or the last three days before slaughter. The decision
alternative we consider are none (i.e., no use of a feed
additive), crude for the first two weeks, crude for three
days pre-slaughter, or purified for three days
pre-slaughter. Each option will have a different expected impact
on the level of campylobacter at slaughter.

The model deployed here implements Reward system 1
and Reward system 2 ([Garcia et al, 2016]). The
user must selects the appropriate reward system as initial input.

Interactive Front-end

Below are some HUGIN widgets for interacting with the model. This
interface has been developed using the HUGIN Web Service API ([Madsen et al, 2013]).

The model is used under the assumption that the
farmer at the time of decision knows the value of a set of risk
factors related to farm characteristics, system variables and
observations. The five risk factors included in the model are shown on
the far left where the user is expected to select the corresponding
value for each risk factore. The middle column shows the decision
alternatives under the heading Feed additive. Below the
decision alternatives the cost, reward and combined cost and reward
are shown. On the far right, the expected impact of the selected
decision alternative is shown.

Farm Characteristics, System Variables and Observations

Select Reward System

Intervention

The expected selling price at slaughter is .
The expected cost of the feed additive is .
This means that the expected profit is .

Decision Impact

The expected logs reduction is
The expected campylobacter level at slaughter is

Example Scenarios

[Garcia et al, 2016] define the best or baseline case
scenario as follows where we have extended the case with information on shared ante room:

The Decision Model

The figure on below shows the structure of the decision model
deployed on this page.

The decision model for campylobacter prevention using a food additive (click image to enlarge)

The purpose of the decision model is to support decision making (under
uncertainty) at the operational level, e.g., to support decisions made
for each chicken flock. The model does not consider decision making at
the more strategic level as, for instance, the use of fly screens. The
model could take into consideration the presence or absence of fly
screens.

The decision model has five main components:

Risk factors: The model includes five risk factors (orange nodes in the graph).

Alternatives: The boxed shaped node in the graph represents the decision node, which has one state for each intervention option including no intervention.

Effectiveness: The blue node implements the efficiency of the intervention in log CFU/g reduction. If there is no intervention, then there is no impact and no reduction.

Cost system: The cost of the intervention. No action has no cost. The costs of the interventions are shown in a table below.

Rewards system: The reward given to the farmer depends on the (measured) level of Campylobactor concentration at slaughter time. The reward system is discussed in the paragraph below.

The hexagon-shaped nodes of a the model are real-valued function
nodes used to either parameterize the model or compute expected
values of interval discrete chance nodes, i.e., computed in the
expected value of a node with states representing bins.

In addition to the graphical structure, the model is quantified using
probabibilities and utilities as discussed in the following two
sections.

The decision problem represented by the model is solved by
determining the policy for the decision node. The policy will identify
the best option to take for each possible scenario (as defined by the
risk factors represented in the model). The policy is a function of
the node having arcs going into the decision node to the states of the
decision nodes (i.e., the decision alternatives).

Cost and Reward System

In their work, [Garcia et al, 2013] and [Garcia et al, 2016] designed two different reward
systems based on the average gross profit for Danish farmers
at the level of DKK 2.92 / chicken (assuming that an an average
broiler chicken from a positive flock in Denmark carries Campylobacter
in a concentration of to 4 to 6 logs CFU/g of sample). The farmer is
assumed to receive a premium for a reduced number of Campylobacter and
a penalty for an increased number of Campylobacter.

Reward system 1 as show in the table below is designed based on a
system implemented in Denmark where the producers get a reward when
the flock is identified as negative at the time of slaughter, [Garcia et al, 2016].

Reward system 2 as show in the table below is an alternative to reward
system 1 where the farmer receives an extra payment for chickens
testing negative for campylobacter, [Garcia et al, 2016].

The cost of each possible intervention included in the model is shown
in the table below. There is no cost associated with the no action,
and the costs / chicken of the three interventions (administrer the
crude feed additive for the first two weeks, the crude
feed additive for the last three days before slaughter, or the
purified feed additive for the last three days before
slaughter) are DKK 0.14, 0.07 and 0.16, respectively.

The model should support the farmer to make the best decision where
considering the use of the feed additive to reduce the concentration
of Campylobacter at slaughter time taking the risk factors such as,
for instance, bio-sercurity, sesonality and other factors of the farms
into consideration.

Probabilities

Each oval node in the decision model represents a random
variable. Each random variable has a probability distribution over its
possible states given each configuration of its parents in the graph,
if any. This means, for instance, that each risk factor has a marginal
distribution while the node representing the effectiveness of the
intervention is conditional on the decision.

The model has two main sets of probability distributions. The first set
relates to the risk factors and the second relates to the
effectiveness of the interventions. The probabilities related to the
risk factors have been computed by [Garcia et al,
2016] using the joint probability distributions provided by [Chowdhury et al, 2012], while the effectiveness of the interventions are
based on simple estimates.

Sensitivity Analysis

The figure below shows the sensitivity of the maximum expected utility
of the decision with respect to each individual finding under the best
case scenario.

The figure below shows the sensitivity of the maximum expected utility
of the decision with respect to each individual finding under the worst
case scenario.

References

[Anon, 2010] Anon, 2010. The Joint Government And Industry Target
To Reduce Campylobacter In Uk Produced Chickens By 2015
December 2010. Available online here.

[EFSA, 2010] Analysis of the baseline survey on the prevalence of Campylobacter in broiler batches
and of Campylobacter and Salmonella on broiler carcasses in the EU, 2008, part A: Campylobacter
and Salmonella prevalence estimates. EFSA Journal 2010; 8(03):1503. Available online here

[Garcia et al, 2016] Garcia, A.B., Madsen, A.L., Vigre, H. (2016). A decision support system for
the control of Campylobacter in chickens at farm level using data from
Denmark, Journal of Agricultural Science, 154, pages 720-731.